Welcome to the InfoSci Platform
Reference Hub4
Cross-Urban Point-of-Interest Recommendation for Non-Natives

Cross-Urban Point-of-Interest Recommendation for Non-Natives

Tao Xu, Yutao Ma, Qian Wang
Copyright: © 2018 |Volume: 15 |Issue: 3 |Pages: 21
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781522542469|DOI: 10.4018/IJWSR.2018070105
Cite Article Cite Article

MLA

Xu, Tao, et al. "Cross-Urban Point-of-Interest Recommendation for Non-Natives." IJWSR vol.15, no.3 2018: pp.82-102. http://doi.org/10.4018/IJWSR.2018070105

APA

Xu, T., Ma, Y., & Wang, Q. (2018). Cross-Urban Point-of-Interest Recommendation for Non-Natives. International Journal of Web Services Research (IJWSR), 15(3), 82-102. http://doi.org/10.4018/IJWSR.2018070105

Chicago

Xu, Tao, Yutao Ma, and Qian Wang. "Cross-Urban Point-of-Interest Recommendation for Non-Natives," International Journal of Web Services Research (IJWSR) 15, no.3: 82-102. http://doi.org/10.4018/IJWSR.2018070105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

This article describes how understanding human mobility behavior is of great significance for predicting a broad range of socioeconomic phenomena in contemporary society. Although many studies have been conducted to uncover behavioral patterns of intra-urban and inter-urban human mobility, a fundamental question remains unanswered: To what degree is human mobility behavior predictable in new cities—a person has never visited before? Location-based social networks with a large volume of check-in records provide an unprecedented opportunity to investigate cross-urban human mobility. The authors' empirical study on millions of records from Foursquare reveals the motives and behavioral patterns of non-natives in 59 cities across the United States. Inspired by the ideology of transfer learning, the authors also propose a machine learning model, which is designed based on the regularities that they found in this study, to predict cross-urban human whereabouts after non-natives move to new cities. The experimental results validate the effectiveness and efficiency of the proposed model, thus allowing us to predict and control activities driven by cross-urban human mobility, such as mobile recommendation, visual (personal) assistant, and epidemic prevention.